This invention relates generally to medical imaging and, in particular, to a method and system for I) reconstructing a model path through a branched tubular organ, II) automatic lymph node station mapping, and III) path or route report generation. Robust and accurate reconstruction of the human airway tree from multi-detector computed-tomography (MDCT) chest scans is vital for many pulmonary-imaging applications. For example, the reconstructed airways serve as input for algorithms that quantify airway morphologyl1-4, generate virtual bronchoscopic (VB) endoluminal renderings5-11, label human airway tree anatomy12-16, and plan routes for bronchoscopic biopsy.14, 17, 18 As modern MDCT scanners can detect hundreds of airway tree branches, manual segmentation and semi-automatic segmentation requiring significant user intervention are impractical for producing a full global segmentation. Fully-automated methods, however, may fail to extract small peripheral airways. These difficulties are exacerbated when trying to define endoluminal airway-wall surfaces, especially in the airway periphery. Such surfaces require a higher level of precision than the segmentation.
An MDCT chest scan is represented by a large 3D array of voxels and associated intensities19. Voxel intensities are measured in Hounsfield units (HU) and scanners are calibrated such that air has intensity around −1000 HU, water around 0 HU, and blood and soft tissue around 50-200 HU20. Thus, airways nominally appear in MDCT scans as tubes of low-intensity airway lumen surrounded by high-intensity walls. Each voxel, however, spans a non-trivial volume measured by the voxel dimensions Δx, Δy, and Δz. Voxels on the lumen/wall boundary may therefore have intermediate intensity between that of air and soft tissue21. This effect is particularly pronounced for peripheral airways with thin walls.
Additional complications arise during MDCT image reconstruction, which involves a choice of convolution kernels. Soft kernels, such as the Siemens B3 I f kernel, have a smoothing effect and tend to blur small airways. Sharp kernels, such as the Siemens B50f and B70f kernels, highlight image gradients but amplify high-frequency noise. Motion artifacts, non-standard patient anatomy, and airway obstructions introduce additional challenges.
Many airway segmentation methods use region-growing algorithms, which attempt to separate air and soft-tissue voxels using an HU threshold22, 23, 12, 24. The final segmented result is a set of air voxels connected to a seed point. Region growing is fast and assumes no prior knowledge of the shape or size of the airways. Choosing an appropriate global HU threshold is difficult, however, as the lungs are filled with air and misclassifying a single wall voxel can allow the segmentation to leak into the lung parenchyma. Filtering the image prior to initializing region growing can mitigate the leakage problem, but filtering removes small peripheral airways24.
Several methods based upon mathematical morphology have also been proposed.1, 25, 24, 26 Such methods typically pass a set of nonlinear filters over the image to locate candidate airway locations. A reconstruction step then rejects false candidates to produce the final segmented result. Morphological approaches are appealing because they scan the entire lung volume. Thus, unlike region-growing algorithms, they can “see” strong airway signals that are not directly connected to a seed voxel. Morphological filters are frequently slow, however, and developing an appropriate reconstruction algorithm has proven to be difficult.
Some recent methods incorporate locally-adaptive segmentation algorithms and seek to detect and stem leakages. Several algorithms propagate a wavefront through the tree. The front splits into smaller, child fronts at bifurcations. Leakage is detected when the front splits too rapidly.27, 28 Tschirren et al. characterize leakages as having a “spongy” texture4. When leakage is detected, locally-adaptive algorithms typically switch to a more conservative set of parameters and re-run the segmentation. Such algorithms can be myopic as the decision to stop segmenting at a particular branch or bifurcation is usually made without benefit of global information.